P967: A MACHINE LEARNING MODEL FOR RISK PREDICTION IN MULTIPLE MYELOMA PROGRESSING AFTER THE FIRST LINE OF THERAPY
Authors: Adrian Mosquera-Orgueira, Marta Sonia Gonzalez Perez, Jose Angel Diaz Arias, Maria-Victoria Mateos
Journal: HemaSphere (2023)

Topic: 14. Myeloma and other monoclonal gammopathies - Clinical Background: Relapsed & refractory multiple myeloma (MM) is mostly an incurable disease and represents an unmet medical need. Most prognostic scores in myeloma have been developed to model patient outcomes from the moment of diagnosis. However, there is a scarcity of prognostic models for those patients who develop relapsed or refractory disease, and traditional models are not fully applicable to this situation. The lack of such information limits the comparability between different clinical trials, as well as the external validity of their results. Furthermore, given the growing landscape of available treatments in relapsed and refractory MM, the possibility to achieve better risk-stratification can be used to optimize individual treatment strategies. Aims: To develop a machine learning model for the prediction of overall survival (OS) for MM patients who relapse after a first line of therapy. Methods: We searched in the CoMMpass database for those patients who had received a second line of anti-myeloma therapy due to previous disease progression. Overall, 283 patients were identified. A total of 72 variables were retrieved for each patient, including age, gender, ECOG, ISS score, proportion of plasma cells in bone marrow and in peripheral blood, first line of treatment, transplant intention, maintenance therapy, duration of the first line, progression-free survival, as well as hemogram, biochemistry and proteinogram variables measured at baseline and at the time of disease progression. OS was defined as time from the initiation of the second line of treatment to the moment of death from any cause. Then, a random forest model was used to model the OS. A variable importance metric was calculated for each model, and those variables with low importance were iteratively removed in order to reduce dimensionality and increment accuracy. Model’s performance was assessed using Harrel’s c-index on out-of-bag estimates of cumulative hazards. Results: Initially, we evaluated a model considering all the 72 variables. This model achieved a c-index of 0.651. Then, we removed those variables with an importance value below 1 x 10-3, rendering a model of 18 variables with a c-index of 0.690. Additional filtering using the same importance threshold reduced the model to 16 variables and a c-index of 0.693. However, by implementing a more stringent importance threshold (5 x 10-3), a model based only on 6 parameters was obtained which achieved a c-index of 0.687. The variables included in this model, by order of importance, were: albumin levels at the time of progression, the duration of the first line, B2-microglobulin at diagnosis, hemoglobin difference between diagnosis and time of progression, hemoglobin at the time of progression and B2-microglobulin at the time of progression. By implementing a more stringent variable importance threshold (5 x 10-3) from the beginning, a 4 variable model was obtained with a c-index of 0.674 (Figure 1). The variables in this model, by order of importance, were: albumin at the time of progression, duration of the first line of therapy, hemoglobin at the time of progression and the difference of hemoglobin between diagnosis and the time of disease progression. Summary/Conclusion: The outcomes of MM patients who progress after a first line of therapy are very heterogeneous. We observed that the most relevant prognostic variables were albumin levels at the time of progression and the duration of the first line of therapy. A new prognostic strategy based on simple data is proposed. This method could be of particular help for the comparison of outcomes between different clinical trials and the analysis of possible sources of biases.Keywords: Multiple myeloma, Risk factor, High risk, Machine learning